Machine Learning in Montpellier, Theory & Practice
Given the current advances in space missions for Earth observation, it is possible to have access to very-high-resolution and multimodal satellite imagery. The data acquired can be optical (e.g., panchromatic, multispectral, and hyperspectral images) or radar, with different synthetic aperture and various trade-offs between resolution and coverage. This offers great application potential in the field of remote sensing. An important role in this context is played by semantic segmentation whose purpose is to assign each pixel in an image to a semantic class, typically related to land cover or land use and with prominent applications in areas such as urban planning, precision agriculture, monitoring of forest species, natural disaster management, and climate change monitoring and mitigation. This presentation focuses on novel methods for the analysis of multimodal data aimed at fully exploiting all the available information, combining ideas from stochastic models and deep learning. On the one hand, deep learning is currently the dominant approach to image classification and segmentation. However, the performances of deep learning methods are remarkably influenced by the quantity and quality of the ground truth used for training. On the other hand, probabilistic graphical models have sparked major interest in the past few years, because of the ever-growing need for structured predictions. Depending on the underlying graph topology over which they are defined, they can effectively model spatial and multiresolution information. The idea is to develop approaches leveraging the advantages of these two major methodological families for the exploitation of multimodal remote sensing data and of the complementary information they convey. The experimental validations, conducted with multimodal multispectral, panchromatic, and radar satellite images, suggest the effectiveness of the proposed methods. Best, Cassio